Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Scientific Reports ◽

10.1038/s41598-019-54696-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

N. T. Devika ◽

Karthik Raman

Keyword(s):

Short Chain Fatty Acids ◽

Acetate Production ◽

Scale Models ◽

Powerful Approach ◽

Metabolic Models ◽

Commercial Applications ◽

Different Strains ◽

Genome Scale ◽

Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

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Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

Microorganisms ◽

10.3390/microorganisms8111793 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1793

Author(s):

Jinxin Zhao ◽

Yan Zhu ◽

Jiru Han ◽

Yu-Wei Lin ◽

Michael Aichem ◽

...

Keyword(s):

Acinetobacter Baumannii ◽

Bloodstream Infection ◽

Post Infection ◽

Metabolic Modeling ◽

Nutrient Utilization ◽

Multidrug Resistant ◽

Host Environment ◽

Gene Essentiality ◽

Genome Scale

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

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BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Nucleic Acids Research ◽

10.1093/nar/gkz1054 ◽

2019 ◽

Cited By ~ 7

Author(s):

Charles J Norsigian ◽

Neha Pusarla ◽

John Luke McConn ◽

James T Yurkovich ◽

Andreas Dräger ◽

...

Keyword(s):

Phylogenetic Tree ◽

Knowledge Base ◽

The Past ◽

Scale Models ◽

Metabolic Models ◽

New Community ◽

Genome Annotations ◽

Genome Scale ◽

High Quality Genome ◽

Strain Genome

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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